Prediction and Influencing Factors of Big Data on College Students' Positive Psychological Quality under Mobile Wireless Network

Abstract

Psychological big data is observed based on behavior or habitual features of people for determining the influencing factors. The influencing factor information is used for analysis in treatment, recommendation, and diagnosis of psychological disorders, depression, etc. Identifying the influencing factors is challenging due to irregular behaviors and responses from young people. However, for organizing the quality of observation, this article introduces a behavioral pattern recognition method (BPRM) with associating quality (AQ) identification. This method observes the different day-to-day behaviors of young people and recurrently associates them. The observations are aided through conventional wireless networks for swift interconnection and information sharing. The association is organized based on the transfer learning state processing model. Based on the state processing, the behavior-based psychological data are classified as abnormal and normal. If the association throughout the state changes remains the same, then it is organized or else the new data are identified as an influencing factor. The state changes are validated using random observation intervals that result in series data associations. Based on the actual data extraction, the proposed method improves the prediction accuracy and reduces false rate and processing time, whereas it improves the organization precision.